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This study aims to evaluate the dynamics between healthcare resource capacity expansion and disease spread. Further, the study estimates the resources required to respond to a pandemic appropriately.

This study adopts a system dynamics simulation and scenario analysis to experiment with the modification of the susceptible exposed infected and recovered (SEIR) model. The experiments evaluate diagnostic capacity expansion to identify suitable expansion plans and timelines. Afterwards, two popularly used forecasting tools, artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA), are used to estimate the requirement of beds for a period when infection data became available.

The results from the study reflect that aggressive testing with isolation and integration of quarantine can be effective strategies to prevent disease outbreaks. The findings demonstrate that decision-makers must rapidly expand the diagnostic capacity during the first two weeks of the outbreak to support aggressive testing and isolation. Further, results confirm a healthcare resource deficit of at least two months for Delhi in the absence of these strategies. Also, the study findings highlight the importance of capacity expansion timelines by simulating a range of contact rates and disease infectivity in the early phase of the outbreak when various parameters are unknown. Further, it has been reflected that forecasting tools can effectively estimate healthcare resource requirements when pandemic data is available.

The models developed in the present study can be utilised by policymakers to suitably design the response plan. The decisions regarding how much diagnostics capacity is needed and when to expand capacity to minimise infection spread have been demonstrated for Delhi city. Also, the study proposed a decision support system (DSS) to assist the decision-maker in short- and long-term planning during the disease outbreak.

The study estimated the resources required for adopting an aggressive testing strategy. Several experiments were performed to successfully validate the robustness of the simulation model. The modification of SEIR model with diagnostic capacity increment, quarantine and testing block has been attempted to provide a distinct perspective on the testing strategy. The prevention of outbreaks has been addressed systematically.

The COVID-19 pandemic significantly increased demand for medical and protective equipment by frontline health workers, as well as the general community, causing the supply chain to stretch beyond capacity, an issue further heightened by geographical and political lockdowns. Various 3D printing technologies were quickly utilised by businesses, institutions and individuals to manufacture a range of products on-demand, close to where they were needed. This study gathered data about 91 3D printed projects initiated prior to April 1, 2020, as the virus spread globally. It found that 60% of products were for personal protective equipment, of which 62% were 3D printed face shields. Fused filament fabrication was the most common 3D print technology used, and websites were the most popular means of centralising project information. The project data provides objective, quantitative insight balanced with qualitative critical review of the broad trends, opportunities and challenges that could be used by governments, health and medical bodies, manufacturing organisations and the 3D printing community to streamline the current response, as well as plan for future crises using a distributed, flexible manufacturing approach.

This paper aims to characterize a sample of Lean Six Sigma (LSS) projects in healthcare settings and discuss some specificities of operational excellence (OPEX) initiatives in hospitals and healthcare organizations in the USA.

A content analysis involving a sample of 23 documents shared by US hospitals was performed in order to achieve the research objectives. Such analysis was based on a conceptual framework developed from the literature review. It was also applied to a quantitative approach, including descriptive statistics, hypothesis testing and correspondence analysis that supported the research.

Most LSSH projects were focused on business transformation and strategic improvements. Simple techniques and tools were predominant such as descriptive statistics, process mapping, 5S and spaghetti charts, usually implemented by Green Belts and Black Belts through the define, measure, analyze, improve and control (DMAIC) method. In addition to the expressive findings reported, these projects' results have been aligned with lead time and operational cost reduction, quality improvement and capacity increase.

The study adds knowledge to the OPEX literature by analyzing the Lean Six Sigma healthcare (LSSH) in hospitals and healthcare institutions in the USA. It also demonstrates that different approaches, such as the kaizen event and DMAIC project show different results according to some techniques and tools applied in the hospital environment.

The empirical evidence presented in this study provides scenery of the LSS practices in the healthcare settings, highlighting the implementation areas, outcomes, tools and techniques mostly used in the North American healthcare institutions.

A new triage method, MBCE (Medical Bio Social Ethics), is presented with social justice, bio, and medical ethics for critical resource distribution during a pandemic. Ethical triage is a complex and challenging process that requires careful consideration of medical, social, cultural, and ethical factors to guide the decision-making process and ensure fair and transparent allocation of resources. When assigning priorities to patients, a clinician would evaluate each patient’s medical condition, age, comorbidities, and prognosis, as well as their cultural and social background and ethical factors.

A statistical analysis shows no interactions among the ethical triage factors. It implies the ethical components have no moderation effect; hence, each is independent. The result also points out that medical and bioethics may have an affinity for interactions. In such cases, there seem to be some ethical factors related to bio and medical ethics that are correlated. Therefore, the triage team should be careful in evaluating patient cases. The algorithm is explained with case histories of the selected patient. A group of triage nurses and general medical practitioners assists with the triage.

The MBCE triage algorithm aims to allocate scarce resources fairly and equitably. Another ethical principle in this triage algorithm is the principle of utility. In a pandemic, the principle of utility may require prioritizing patients with a higher likelihood of survival or requiring less medical care. The research presents a sensitivity analysis of a patient’s triage score to show the algorithm’s robustness. A weighted score of ethical factors combined with an assessment of triage factors combines multiple objectives to assign a fair triage score. These distinctive features of the algorithm are reasonably easy to implement and a new direction for the unbiased triage principle.

The idea is to make decisions about distributing and using scarce medical resources. Triage algorithms raise ethical issues, such as discrimination and justice, guiding medical ethics in treating patients with terminal diseases or comorbidity. One of the main ethical principles in triage algorithms is the principle of distributive justice.

Advancements in responsive manufacturing have been supporting companies over the last few decades. However, manufacturers now operate in a context of continuous uncertainty. This research paper explores a mechanism where companies can “elastically” provision and deprovision their production capacity, to enable them in coping with repeated disruptions. Such a mechanism is facilitated by the imitability and substitutability of production resources.

An inductive study was conducted using Gioia methodology for this theory generation research. Respondents from 20 UK manufacturing companies across multiple industrial sectors reflected on their experience during COVID-19. Resource-based view and resource dependence theory were employed to analyse the manufacturers' use of internal and external production resources.

The study identifies elastic responses at four operational levels: production-line, factory, company and supply chain. Elastic responses that imposed variable-costs were particularly well-suited for coping with unforeseen disruptions. Further, the imitability and substitutability of manufacturers helped others produce alternate goods during the crisis.

While uniqueness of production capability helps manufacturers sustain competitive advantage against competitors during stable operations, imitability and substitutability are beneficial during a crisis. Successful manufacturing companies need to combine these two approaches to respond effectively to repeated disruptions in a context of ongoing uncertainties. The theoretical contribution is in characterising responsive manufacturing in terms of resource heterogeneity and resource homogeneity, with elastic resourcing as the underlying mechanism.

The research aims to explore the wisdom, knowledge and practices in vernacular housing settlements with their sustainability underpinnings as tools for modelling rural affordable housing in tropical regions. The study is based on a weaving settlement in Bargarh district of Odisha, which is globally acclaimed for its Ikkat style of weaving.

A hierarchical framework of sustainability resting on the three pillars of ecological, economical and environmental dimensions is derived from existing theoretical research. This framework of 22 indicators is subsequently assigned to assess the sustainability of the vernacular weavers' settlement through quantitative evaluation. A qualitative assessment through observation and deduction also verifies the result.

Since the vernacular weavers settlement performs very well on the sustainability scorecard, the paper suggests that its best practices can be incorporated while designing affordable housing so that social, cultural and heritage values are retained and a climate conscious, energy-efficient sustainable approach is ensured.

The recommendations from the assessment has many lessons while framing policies for rural affordable housing as it cannot have one size that fits all settlement typology irrespective of the occupational, climatic and social needs.

The sustainable design and planning principles embedded in this vernacular settlement offers a valuable blueprint to re-imagine the affordable housing in rural areas which can be myopic if it does not take into account the occupational needs and life style of craftsmen dwellers.

The present study was designed to explore the major challenges being faced by the Indian nurses' pre-post pandemic period affecting their well-being (WB) and identify factors that motivated them to perform their service wholeheartedly during the pandemic. The study also tries to bridge the gap in the study area by providing various ways that can help maintain the WB of health care professionals.

A descriptive exploratory qualitative design involving semi-structured interviews was conducted during December–January 2021 with 30 nurses from hospitals in Punjab Qualitative and thematic data analysis technique were adopted. In addition, a literature review was also conducted to study the various factors that affect the WB of health care professionals.

There are various themes and subthemes that were identified by the health care professionals, such as (1) psychological WB, (2) social WB and (3) workplace WB and (4) key motivators. This research work has identified various managerial implications that can play a huge rolein strengthening the healthcare sector of the entire world economy, paving the way toward the better WB of healthcare professionals (HCPs).

Firstly, it is probably the only study that is performed on nursing staff to evaluate their personal experiences during crucial times. It has successfully compared the factors affecting WB pre- and post-pandemic, leading to the emergence of many new factors that have originated due to the pandemic and are the cause of the poor WB of HCPs (Figures 2, 4). Secondly, it is the only study that targeted only those nurses who have provided their services in both scenarios. Finally, the study has been a pioneer in identifying the importance of maintaining the WB of HCPs at hospitals.

This paper aims to review and critically assess the role that data visualizations played as communication media tools to help society during a worldwide crisis. This paper re-creates and analyzes several visualizations, critically and ethically assesses their strengths and limitations and provides a set of best practices that are informative, accurate, ethical and engaging at each stage in a reader’s interest.

The paper bases its methodology on the construct of “The Network Society” (Van Dijk, 2006; Castells, 2000, 2006) by creating a series of social networked visualizations, identifying the challenges and pitfalls associated with this communication approach and suggesting best practices in information communication technology. The case study is COVID-19.

The research in this study found that visual data dashboards and interactive Web-based charts did play a significant role in helping society understand COVID-19’s impact to make better informed decisions about society’s health and safety.

Visual expositions of data do have strengths and weaknesses depending on how they are designed, how they communicate the story and how they are ethically deployed. Best practices are provided to help mitigate these limitations.

Visualizations are certainly not new, but the technology for rapidly developing and sharing them is new. Visual expositions provide an effective media for communicating complex information to a networked society.

Visual expositions provide an effective media for communicating complex information to a networked society.

This paper highlights the significance of the need to understand complex data in a crisis in a visual format and to communicate the information quickly, persuasively, effectively and ethically to a networked audience.

The purpose of this study is to propose a new mapping tool called Circular Value Stream Mapping (C-VSM) that combines Circular Economy principles with Lean tools to enhance sustainability performance in operations.

To develop the C-VSM tool, the researchers conducted a literature review and a focus group. The tool was then applied to two real case studies in the agri-food sector, specifically analyzing an artichoke and olive oil producer, to assess its validity and effectiveness.

The study introduces the Circular Resource Box (CRB) as a key innovation in the C-VSM tool. This visual representation effectively captures resource circularity and how resources and wastes are managed, making it easy to identify circularity in the production process. By combining qualitative and quantitative information with this visual representation, companies can identify improvement opportunities aligned with the CE.

The research is limited in scope as it focuses on the application of the C-VSM tool in the agri-food sector. Further research could explore its applicability in other industries and settings to understand its broader impact.

The C-VSM tool provides practical benefits to companies seeking to transition from linear to circular production processes. It enables practitioners to identify opportunities to reduce environmental impacts and optimize production operations in line with CE.

The introduction of the C-VSM tool is a novel approach that bridges the gap between Lean Manufacturing and CE concepts, advancing the understanding of how CE thinking can be effectively implemented in operations.

The COVID-19 pandemic has posed many challenges in almost all sectors around the globe. Because of the pandemic, government entities responsible for managing health-care resources face challenges in managing and distributing their limited and valuable health resources. In addition, severe outbreaks may occur in a small or large geographical area. Therefore, county-level preparation is crucial for officials and organizations who manage such disease outbreaks. However, most COVID-19-related research projects have focused on either state- or country-level. Only a few studies have considered county-level preparations, such as identifying high-risk counties of a particular state to fight against the COVID-19 pandemic. Therefore, the purpose of this research is to prioritize counties in a state based on their COVID-19-related risks to manage the COVID outbreak effectively.

In this research, the authors use a systematic hybrid approach that uses a clustering technique to group counties that share similar COVID conditions and use a multi-criteria decision-making approach – the analytic hierarchy process – to rank clusters with respect to the severity of the pandemic. The clustering was performed using two methods, k-means and fuzzy c-means, but only one of them was used at a time during the experiment.

The results of this study indicate that the proposed approach can effectively identify and rank the most vulnerable counties in a particular state. Hence, state health resources managing entities can identify counties in desperate need of more attention before they allocate their resources and better prepare those counties before another surge.

To the best of the authors’ knowledge, this study is the first to use both an unsupervised learning approach and the analytic hierarchy process to identify and rank state counties in accordance with the severity of COVID-19.